摘要
植被碳储量估测是自然资源监测的重要内容,遥感技术结合地面样地进行反演可以获得区域范围内植被碳储量的空间连续分布,弥补了传统人工抽样调查估测的不足。然而,现有的参数和非参数遥感估测模型大多忽略了样地数据的变异与空间自相关关系。研究以Landsat 8 OLI影像为数据源提取遥感变量,结合植被碳储量实测调查数据,利用最小信息准则(AICc)、最大空间自相关距离(MSAD)和交叉验证(CV)分别确定最优带宽,组合Gaussian、Bi-square和Exponential核函数构建地理加权回归(GWR)模型估算深圳市植被碳储量,并与多元线性回归(MLR)进行比较,选择最优模型绘制深圳市植被碳储量空间分布图。研究结果表明,GWR模型整体精度优于MLR模型,GWR模型的决定系数(R;)均高于MLR模型,且均方根误差(RMSE)和平均绝对误差(MAE)显著降低。带宽和核函数的选择对GWR模型估测结果产生了显著影响。以CV确定带宽、Exponential为核函数组合构建的GWR模型效果最佳,其R;为0.697,RMSE为10.437 Mg C/hm^(2),相比其它模型精度上升了13.87%—32.28%,且变量回归参数均存在显著空间非平稳性,能较好反映空间异质性。结合最优GWR模型和植被类型空间分布估算深圳市植被碳储量,得到其碳储量值在1.63—60.95 Mg C/hm^(2)之间,其中高值和低值主要分布于森林和草地区域,与深圳市植被覆盖情况基本一致。GWR模型考虑了变量空间异质性,基于带宽优选的GWR模型一定程度上能获得更合理的碳储量空间分布,能为深圳市植被碳储量遥感估算提供方法与技术参考。
Vegetation carbon storage estimation is an important part of natural resources monitoring. Remote sensing technology combined with ground sample can obtain the spatial continuous distribution of vegetation carbon reserves in the region, which makes up for the deficiency of the traditional artificial sampling survey estimation. However, most of the existing parameter and nonparameter remote sensing estimation models ignore the relationship between sample data variation and spatial autocorrelation. In this study, Landsat 8 OLI images were used as data sources to extract remote sensing variables, combined with the survey data of vegetation carbon storage in ShenZhen. Three metrics, including Akaike Information Criterion corrected(AICc), maximum spatial autocorrelation distance(MSAD), and Cross-Validation(CV), were used to determine the optimal bandwidth. Different geographically weighted regression(GWR) models inversing carbon reserve were constructed using Gaussian, Bi-square and Exponential as kernel function, respectively. Compared with the multiple linear regression(MLR), the optimal model was selected to make the spatial distribution map of vegetation carbon storage in Shenzhen. The results showed that the overall precision of the GWR models were better than that of the MLR model. The coefficient of determination(R~2) of the GWR models were higher than that of MLR model, and the root mean square error(RMSE) and mean absolute error(MAE) were reduced significantly. The selection of bandwidth and kernel function can have significant influence on the estimation results of GWR model. Among the constructed GWR models, the GWR model with cross-validation determined bandwidth and exponential as the kernel was the best, with R~2 of 0.697 and RMSE of 10.437 Mg C/hm^(2), which increased by 13.87% to 32.28% on model accuracy compared with other models. And the variable regression parameters of this model had significantly spatial non-stationarity, which could better reflect spatial heterogeneity. Combined with the optimal GWR model and the spatial distribution of vegetation types, the carbon storage of vegetation in Shenzhen was estimated, and the carbon storage values were between 1.63 and 60.95 Mg C/hm^(2). The high and low carbon storage were mainly distributed in forest and grassland areas, which was basically consistent with the vegetation coverage in Shenzhen. Considering the spatial heterogeneity of variables, the GWR model based on bandwidth optimization can obtain more reasonable spatial distribution of vegetation carbon storage to some extent, which can provide a method and technical reference for remote sensing estimation of vegetation carbon storage in Shenzhen.
作者
龙依
蒋馥根
孙华
王天宏
邹琪
陈川石
LONG Yi;JIANG Fugen;SUN Hua;WANG Tianhong;ZOU Qi;CHEN Chuanshi(Research Center of Forestry Remote Sensing&Information Engineering,Central South University of Forestry and Technology,Changsha 410004,China;Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province,Changsha 410004,China;Key Laboratory of National Forestry&Grassland Administration on Forest Resources Management and Monitoring in Southern Area,Changsha 410004,China;Central South Survey and Planning Design Institute of State Forestry and Grassland Administration,Changsha 410004,China)
出处
《生态学报》
CAS
CSCD
北大核心
2022年第12期4933-4945,共13页
Acta Ecologica Sinica
基金
湖南省教育厅重点项目(17A225)
国家自然科学基金面上项目(31971578)
湖南省林业科技创新专项(XLK201986)
长沙市科技计划项目(kq2004095)。
关键词
遥感反演
植被碳储量
局部模型
地理加权回归
Landsat
8
remote sensing inversion
vegetation carbon storage
local model
geographically weighted regression
Landsat 8
